Overview

Dataset statistics

Number of variables60
Number of observations442576
Missing cells14620255
Missing cells (%)55.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory202.6 MiB
Average record size in memory480.0 B

Variable types

Numeric32
Categorical28

Alerts

SURVYEAR has constant value "2024"Constant
LKPUBAG has constant value "1.0"Constant
LKEMPLOY has constant value "1.0"Constant
LKRELS has constant value "1.0"Constant
LKATADS has constant value "1.0"Constant
LKANSADS has constant value "1.0"Constant
LKOTHERN has constant value "1.0"Constant
AGE_12 is highly overall correlated with AGE_6 and 4 other fieldsHigh correlation
AGE_6 is highly overall correlated with AGE_12 and 3 other fieldsHigh correlation
AGYOWNK is highly overall correlated with AGE_12High correlation
AHRSMAIN is highly overall correlated with ATOTHRS and 6 other fieldsHigh correlation
ATOTHRS is highly overall correlated with AHRSMAIN and 6 other fieldsHigh correlation
AVAILABL is highly overall correlated with LFSSTAT and 3 other fieldsHigh correlation
COWMAIN is highly overall correlated with EVERWORK and 1 other fieldsHigh correlation
DURJLESS is highly overall correlated with AGE_12 and 3 other fieldsHigh correlation
DURUNEMP is highly overall correlated with LFSSTATHigh correlation
EDUC is highly overall correlated with AGE_6High correlation
ESTSIZE is highly overall correlated with FIRMSIZEHigh correlation
EVERWORK is highly overall correlated with COWMAIN and 3 other fieldsHigh correlation
FIRMSIZE is highly overall correlated with ESTSIZEHigh correlation
FLOWUNEM is highly overall correlated with LFSSTAT and 3 other fieldsHigh correlation
FTPTLAST is highly overall correlated with DURJLESS and 1 other fieldsHigh correlation
FTPTMAIN is highly overall correlated with AGE_6 and 4 other fieldsHigh correlation
HRLYEARN is highly overall correlated with AGE_6High correlation
HRSAWAY is highly overall correlated with LFSSTATHigh correlation
LFSSTAT is highly overall correlated with AHRSMAIN and 11 other fieldsHigh correlation
NOC_10 is highly overall correlated with NOC_43High correlation
NOC_43 is highly overall correlated with NOC_10High correlation
PAIDOT is highly overall correlated with XTRAHRSHigh correlation
PAYAWAY is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
PREVTEN is highly overall correlated with AGE_12High correlation
PRIORACT is highly overall correlated with AVAILABL and 2 other fieldsHigh correlation
TENURE is highly overall correlated with AGE_12High correlation
TLOLOOK is highly overall correlated with AVAILABL and 4 other fieldsHigh correlation
UHRSMAIN is highly overall correlated with AHRSMAIN and 3 other fieldsHigh correlation
UNEMFTPT is highly overall correlated with AVAILABL and 2 other fieldsHigh correlation
UNION is highly overall correlated with COWMAINHigh correlation
UNPAIDOT is highly overall correlated with XTRAHRSHigh correlation
UTOTHRS is highly overall correlated with AHRSMAIN and 3 other fieldsHigh correlation
WHYLEFTN is highly overall correlated with WHYLEFTOHigh correlation
WHYLEFTO is highly overall correlated with WHYLEFTNHigh correlation
XTRAHRS is highly overall correlated with PAIDOT and 1 other fieldsHigh correlation
YABSENT is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
YAWAY is highly overall correlated with LFSSTATHigh correlation
MJH is highly imbalanced (68.7%)Imbalance
PERMTEMP is highly imbalanced (68.8%)Imbalance
AVAILABL is highly imbalanced (59.3%)Imbalance
SCHOOLN is highly imbalanced (56.3%)Imbalance
AGE_6 has 355250 (80.3%) missing valuesMissing
MJH has 184245 (41.6%) missing valuesMissing
EVERWORK has 258331 (58.4%) missing valuesMissing
FTPTLAST has 410825 (92.8%) missing valuesMissing
COWMAIN has 152494 (34.5%) missing valuesMissing
NAICS_21 has 152494 (34.5%) missing valuesMissing
NOC_10 has 152494 (34.5%) missing valuesMissing
NOC_43 has 152494 (34.5%) missing valuesMissing
YABSENT has 421163 (95.2%) missing valuesMissing
WKSAWAY has 421163 (95.2%) missing valuesMissing
PAYAWAY has 423510 (95.7%) missing valuesMissing
UHRSMAIN has 184245 (41.6%) missing valuesMissing
AHRSMAIN has 184245 (41.6%) missing valuesMissing
FTPTMAIN has 184245 (41.6%) missing valuesMissing
UTOTHRS has 184245 (41.6%) missing valuesMissing
ATOTHRS has 184245 (41.6%) missing valuesMissing
HRSAWAY has 236312 (53.4%) missing valuesMissing
YAWAY has 415132 (93.8%) missing valuesMissing
PAIDOT has 236312 (53.4%) missing valuesMissing
UNPAIDOT has 236312 (53.4%) missing valuesMissing
XTRAHRS has 236312 (53.4%) missing valuesMissing
WHYPT has 393838 (89.0%) missing valuesMissing
TENURE has 184245 (41.6%) missing valuesMissing
PREVTEN has 410825 (92.8%) missing valuesMissing
HRLYEARN has 218784 (49.4%) missing valuesMissing
UNION has 218784 (49.4%) missing valuesMissing
PERMTEMP has 218784 (49.4%) missing valuesMissing
ESTSIZE has 218784 (49.4%) missing valuesMissing
FIRMSIZE has 218784 (49.4%) missing valuesMissing
DURUNEMP has 426231 (96.3%) missing valuesMissing
FLOWUNEM has 425429 (96.1%) missing valuesMissing
UNEMFTPT has 425429 (96.1%) missing valuesMissing
WHYLEFTO has 410825 (92.8%) missing valuesMissing
WHYLEFTN has 410825 (92.8%) missing valuesMissing
DURJLESS has 288071 (65.1%) missing valuesMissing
AVAILABL has 422834 (95.5%) missing valuesMissing
LKPUBAG has 440335 (99.5%) missing valuesMissing
LKEMPLOY has 434674 (98.2%) missing valuesMissing
LKRELS has 437169 (98.8%) missing valuesMissing
LKATADS has 430293 (97.2%) missing valuesMissing
LKANSADS has 435503 (98.4%) missing valuesMissing
LKOTHERN has 438545 (99.1%) missing valuesMissing
PRIORACT has 427053 (96.5%) missing valuesMissing
YNOLOOK has 436914 (98.7%) missing valuesMissing
TLOLOOK has 441754 (99.8%) missing valuesMissing
SCHOOLN has 112181 (25.3%) missing valuesMissing
AGYOWNK has 327294 (74.0%) missing valuesMissing
CMA has 284362 (64.3%) zerosZeros
EDUC has 16917 (3.8%) zerosZeros
AHRSMAIN has 21570 (4.9%) zerosZeros
ATOTHRS has 21413 (4.8%) zerosZeros
HRSAWAY has 178820 (40.4%) zerosZeros
PAIDOT has 187593 (42.4%) zerosZeros
UNPAIDOT has 188588 (42.6%) zerosZeros
XTRAHRS has 170981 (38.6%) zerosZeros

Reproduction

Analysis started2024-05-17 19:17:58.104093
Analysis finished2024-05-17 19:21:08.575294
Duration3 minutes and 10.47 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

REC_NUM
Real number (ℝ)

Distinct112084
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55327.983
Minimum1
Maximum112084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:08.686336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5533
Q127661.75
median55322.5
Q382983.25
95-th percentile105112
Maximum112084
Range112083
Interquartile range (IQR)55321.5

Descriptive statistics

Standard deviation31949.707
Coefficient of variation (CV)0.57746018
Kurtosis-1.1985713
Mean55327.983
Median Absolute Deviation (MAD)27661
Skewness0.0010302506
Sum2.4486838 × 1010
Variance1.0207838 × 109
MonotonicityNot monotonic
2024-05-17T15:21:08.816271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
< 0.1%
72862 4
 
< 0.1%
72860 4
 
< 0.1%
72859 4
 
< 0.1%
72858 4
 
< 0.1%
72857 4
 
< 0.1%
72856 4
 
< 0.1%
72855 4
 
< 0.1%
72854 4
 
< 0.1%
72853 4
 
< 0.1%
Other values (112074) 442536
> 99.9%
ValueCountFrequency (%)
1 4
< 0.1%
2 4
< 0.1%
3 4
< 0.1%
4 4
< 0.1%
5 4
< 0.1%
6 4
< 0.1%
7 4
< 0.1%
8 4
< 0.1%
9 4
< 0.1%
10 4
< 0.1%
ValueCountFrequency (%)
112084 1
< 0.1%
112083 1
< 0.1%
112082 1
< 0.1%
112081 1
< 0.1%
112080 1
< 0.1%
112079 1
< 0.1%
112078 1
< 0.1%
112077 1
< 0.1%
112076 1
< 0.1%
112075 1
< 0.1%

SURVYEAR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2024
442576 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1770304
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 442576
100.0%

Length

2024-05-17T15:21:08.930255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:09.039154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2024 442576
100.0%

Most occurring characters

ValueCountFrequency (%)
2 885152
50.0%
0 442576
25.0%
4 442576
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1770304
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 885152
50.0%
0 442576
25.0%
4 442576
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1770304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 885152
50.0%
0 442576
25.0%
4 442576
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1770304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 885152
50.0%
0 442576
25.0%
4 442576
25.0%

SURVMNTH
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
4
112084 
3
111282 
2
109932 
1
109278 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters442576
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 112084
25.3%
3 111282
25.1%
2 109932
24.8%
1 109278
24.7%

Length

2024-05-17T15:21:09.127358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:09.242172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 112084
25.3%
3 111282
25.1%
2 109932
24.8%
1 109278
24.7%

Most occurring characters

ValueCountFrequency (%)
4 112084
25.3%
3 111282
25.1%
2 109932
24.8%
1 109278
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 112084
25.3%
3 111282
25.1%
2 109932
24.8%
1 109278
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common 442576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 112084
25.3%
3 111282
25.1%
2 109932
24.8%
1 109278
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 112084
25.3%
3 111282
25.1%
2 109932
24.8%
1 109278
24.7%

LFSSTAT
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1
236918 
4
167098 
2
 
21413
3
 
17147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters442576
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 236918
53.5%
4 167098
37.8%
2 21413
 
4.8%
3 17147
 
3.9%

Length

2024-05-17T15:21:09.346879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:09.460219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 236918
53.5%
4 167098
37.8%
2 21413
 
4.8%
3 17147
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 236918
53.5%
4 167098
37.8%
2 21413
 
4.8%
3 17147
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 236918
53.5%
4 167098
37.8%
2 21413
 
4.8%
3 17147
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 442576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 236918
53.5%
4 167098
37.8%
2 21413
 
4.8%
3 17147
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 236918
53.5%
4 167098
37.8%
2 21413
 
4.8%
3 17147
 
3.9%

PROV
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.456394
Minimum10
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:09.551509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q124
median35
Q347
95-th percentile59
Maximum59
Range49
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.561933
Coefficient of variation (CV)0.42261919
Kurtosis-0.83348135
Mean34.456394
Median Absolute Deviation (MAD)11
Skewness0.013460047
Sum15249573
Variance212.0499
MonotonicityNot monotonic
2024-05-17T15:21:09.654078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35 144891
32.7%
24 82983
18.8%
59 54585
 
12.3%
48 32808
 
7.4%
46 29553
 
6.7%
47 25359
 
5.7%
13 21518
 
4.9%
12 21028
 
4.8%
10 20245
 
4.6%
11 9606
 
2.2%
ValueCountFrequency (%)
10 20245
 
4.6%
11 9606
 
2.2%
12 21028
 
4.8%
13 21518
 
4.9%
24 82983
18.8%
35 144891
32.7%
46 29553
 
6.7%
47 25359
 
5.7%
48 32808
 
7.4%
59 54585
 
12.3%
ValueCountFrequency (%)
59 54585
 
12.3%
48 32808
 
7.4%
47 25359
 
5.7%
46 29553
 
6.7%
35 144891
32.7%
24 82983
18.8%
13 21518
 
4.9%
12 21028
 
4.8%
11 9606
 
2.2%
10 20245
 
4.6%

CMA
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7487347
Minimum0
Maximum9
Zeros284362
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:09.753704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7855456
Coefficient of variation (CV)1.592892
Kurtosis0.77714281
Mean1.7487347
Median Absolute Deviation (MAD)0
Skewness1.4312238
Sum773948
Variance7.7592641
MonotonicityNot monotonic
2024-05-17T15:21:09.844113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 284362
64.3%
4 45683
 
10.3%
2 26156
 
5.9%
9 25474
 
5.8%
6 15971
 
3.6%
5 10030
 
2.3%
1 9502
 
2.1%
8 9018
 
2.0%
7 8219
 
1.9%
3 8161
 
1.8%
ValueCountFrequency (%)
0 284362
64.3%
1 9502
 
2.1%
2 26156
 
5.9%
3 8161
 
1.8%
4 45683
 
10.3%
5 10030
 
2.3%
6 15971
 
3.6%
7 8219
 
1.9%
8 9018
 
2.0%
9 25474
 
5.8%
ValueCountFrequency (%)
9 25474
 
5.8%
8 9018
 
2.0%
7 8219
 
1.9%
6 15971
 
3.6%
5 10030
 
2.3%
4 45683
 
10.3%
3 8161
 
1.8%
2 26156
 
5.9%
1 9502
 
2.1%
0 284362
64.3%

AGE_12
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.230914
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:09.938897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5936214
Coefficient of variation (CV)0.49698024
Kurtosis-1.2456386
Mean7.230914
Median Absolute Deviation (MAD)3
Skewness-0.18273345
Sum3200229
Variance12.914115
MonotonicityNot monotonic
2024-05-17T15:21:10.033765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 77037
17.4%
10 39622
9.0%
6 35693
8.1%
11 35144
7.9%
5 34854
7.9%
9 34318
7.8%
4 33868
7.7%
8 32796
7.4%
7 31918
7.2%
1 29527
 
6.7%
Other values (2) 57799
13.1%
ValueCountFrequency (%)
1 29527
6.7%
2 28499
6.4%
3 29300
6.6%
4 33868
7.7%
5 34854
7.9%
6 35693
8.1%
7 31918
7.2%
8 32796
7.4%
9 34318
7.8%
10 39622
9.0%
ValueCountFrequency (%)
12 77037
17.4%
11 35144
7.9%
10 39622
9.0%
9 34318
7.8%
8 32796
7.4%
7 31918
7.2%
6 35693
8.1%
5 34854
7.9%
4 33868
7.7%
3 29300
 
6.6%

AGE_6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing355250
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean3.5893548
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:10.123578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276196
Coefficient of variation (CV)0.48131759
Kurtosis-1.2986028
Mean3.5893548
Median Absolute Deviation (MAD)2
Skewness-0.0044681627
Sum313444
Variance2.9846695
MonotonicityNot monotonic
2024-05-17T15:21:10.221071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 18008
 
4.1%
2 17073
 
3.9%
4 16839
 
3.8%
1 12454
 
2.8%
3 11660
 
2.6%
5 11292
 
2.6%
(Missing) 355250
80.3%
ValueCountFrequency (%)
1 12454
2.8%
2 17073
3.9%
3 11660
2.6%
4 16839
3.8%
5 11292
2.6%
6 18008
4.1%
ValueCountFrequency (%)
6 18008
4.1%
5 11292
2.6%
4 16839
3.8%
3 11660
2.6%
2 17073
3.9%
1 12454
2.8%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2
226731 
1
215845 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters442576
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 226731
51.2%
1 215845
48.8%

Length

2024-05-17T15:21:10.322726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:10.431053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 226731
51.2%
1 215845
48.8%

Most occurring characters

ValueCountFrequency (%)
2 226731
51.2%
1 215845
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 226731
51.2%
1 215845
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common 442576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 226731
51.2%
1 215845
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 226731
51.2%
1 215845
48.8%

MARSTAT
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8696088
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:10.511915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.1716966
Coefficient of variation (CV)0.75679184
Kurtosis-1.5019881
Mean2.8696088
Median Absolute Deviation (MAD)1
Skewness0.55764743
Sum1270020
Variance4.7162659
MonotonicityNot monotonic
2024-05-17T15:21:10.605286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 212680
48.1%
6 121095
27.4%
2 51971
 
11.7%
3 23463
 
5.3%
5 22971
 
5.2%
4 10396
 
2.3%
ValueCountFrequency (%)
1 212680
48.1%
2 51971
 
11.7%
3 23463
 
5.3%
4 10396
 
2.3%
5 22971
 
5.2%
6 121095
27.4%
ValueCountFrequency (%)
6 121095
27.4%
5 22971
 
5.2%
4 10396
 
2.3%
3 23463
 
5.3%
2 51971
 
11.7%
1 212680
48.1%

EDUC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.458023
Minimum0
Maximum6
Zeros16917
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:10.694621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6318347
Coefficient of variation (CV)0.47189817
Kurtosis-0.85325588
Mean3.458023
Median Absolute Deviation (MAD)1
Skewness-0.34743804
Sum1530438
Variance2.6628846
MonotonicityNot monotonic
2024-05-17T15:21:10.785014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 147056
33.2%
2 85268
19.3%
5 81017
18.3%
1 47381
 
10.7%
6 41467
 
9.4%
3 23470
 
5.3%
0 16917
 
3.8%
ValueCountFrequency (%)
0 16917
 
3.8%
1 47381
 
10.7%
2 85268
19.3%
3 23470
 
5.3%
4 147056
33.2%
5 81017
18.3%
6 41467
 
9.4%
ValueCountFrequency (%)
6 41467
 
9.4%
5 81017
18.3%
4 147056
33.2%
3 23470
 
5.3%
2 85268
19.3%
1 47381
 
10.7%
0 16917
 
3.8%

MJH
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing184245
Missing (%)41.6%
Memory size3.4 MiB
1.0
243783 
2.0
 
14548

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters774993
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 243783
55.1%
2.0 14548
 
3.3%
(Missing) 184245
41.6%

Length

2024-05-17T15:21:10.890630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:10.990424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 243783
94.4%
2.0 14548
 
5.6%

Most occurring characters

ValueCountFrequency (%)
. 258331
33.3%
0 258331
33.3%
1 243783
31.5%
2 14548
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 516662
66.7%
Other Punctuation 258331
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258331
50.0%
1 243783
47.2%
2 14548
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 258331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 774993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 258331
33.3%
0 258331
33.3%
1 243783
31.5%
2 14548
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 774993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 258331
33.3%
0 258331
33.3%
1 243783
31.5%
2 14548
 
1.9%

EVERWORK
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing258331
Missing (%)58.4%
Memory size3.4 MiB
2.0
122754 
1.0
31751 
3.0
29740 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters552735
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 122754
27.7%
1.0 31751
 
7.2%
3.0 29740
 
6.7%
(Missing) 258331
58.4%

Length

2024-05-17T15:21:11.079381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:11.193093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 122754
66.6%
1.0 31751
 
17.2%
3.0 29740
 
16.1%

Most occurring characters

ValueCountFrequency (%)
. 184245
33.3%
0 184245
33.3%
2 122754
22.2%
1 31751
 
5.7%
3 29740
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 368490
66.7%
Other Punctuation 184245
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 184245
50.0%
2 122754
33.3%
1 31751
 
8.6%
3 29740
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 184245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 552735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 184245
33.3%
0 184245
33.3%
2 122754
22.2%
1 31751
 
5.7%
3 29740
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 552735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 184245
33.3%
0 184245
33.3%
2 122754
22.2%
1 31751
 
5.7%
3 29740
 
5.4%

FTPTLAST
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing410825
Missing (%)92.8%
Memory size3.4 MiB
1.0
20932 
2.0
10819 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters95253
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 20932
 
4.7%
2.0 10819
 
2.4%
(Missing) 410825
92.8%

Length

2024-05-17T15:21:11.293697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:11.399206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 20932
65.9%
2.0 10819
34.1%

Most occurring characters

ValueCountFrequency (%)
. 31751
33.3%
0 31751
33.3%
1 20932
22.0%
2 10819
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63502
66.7%
Other Punctuation 31751
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31751
50.0%
1 20932
33.0%
2 10819
 
17.0%
Other Punctuation
ValueCountFrequency (%)
. 31751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 31751
33.3%
0 31751
33.3%
1 20932
22.0%
2 10819
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 31751
33.3%
0 31751
33.3%
1 20932
22.0%
2 10819
 
11.4%

COWMAIN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing152494
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean2.1190629
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:11.478175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.187001
Coefficient of variation (CV)0.56015373
Kurtosis4.7095117
Mean2.1190629
Median Absolute Deviation (MAD)0
Skewness2.1856244
Sum614702
Variance1.4089713
MonotonicityNot monotonic
2024-05-17T15:21:11.565670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 185928
42.0%
1 67726
 
15.3%
6 17398
 
3.9%
4 8824
 
2.0%
3 8042
 
1.8%
5 1919
 
0.4%
7 245
 
0.1%
(Missing) 152494
34.5%
ValueCountFrequency (%)
1 67726
 
15.3%
2 185928
42.0%
3 8042
 
1.8%
4 8824
 
2.0%
5 1919
 
0.4%
6 17398
 
3.9%
7 245
 
0.1%
ValueCountFrequency (%)
7 245
 
0.1%
6 17398
 
3.9%
5 1919
 
0.4%
4 8824
 
2.0%
3 8042
 
1.8%
2 185928
42.0%
1 67726
 
15.3%

IMMIG
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
3
342931 
2
70689 
1
 
28956

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters442576
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 342931
77.5%
2 70689
 
16.0%
1 28956
 
6.5%

Length

2024-05-17T15:21:11.672801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:11.784156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 342931
77.5%
2 70689
 
16.0%
1 28956
 
6.5%

Most occurring characters

ValueCountFrequency (%)
3 342931
77.5%
2 70689
 
16.0%
1 28956
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 342931
77.5%
2 70689
 
16.0%
1 28956
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 442576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 342931
77.5%
2 70689
 
16.0%
1 28956
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 342931
77.5%
2 70689
 
16.0%
1 28956
 
6.5%

NAICS_21
Real number (ℝ)

MISSING 

Distinct21
Distinct (%)< 0.1%
Missing152494
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean13.198702
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:11.881461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median14
Q317
95-th percentile21
Maximum21
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.019118
Coefficient of variation (CV)0.3802736
Kurtosis-0.86406952
Mean13.198702
Median Absolute Deviation (MAD)4
Skewness-0.30666502
Sum3828706
Variance25.191546
MonotonicityNot monotonic
2024-05-17T15:21:11.984780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
17 41255
 
9.3%
10 31839
 
7.2%
16 23701
 
5.4%
14 23040
 
5.2%
6 22892
 
5.2%
21 17953
 
4.1%
19 16537
 
3.7%
11 15263
 
3.4%
12 12697
 
2.9%
7 12087
 
2.7%
Other values (11) 72818
16.5%
(Missing) 152494
34.5%
ValueCountFrequency (%)
1 4474
 
1.0%
2 1012
 
0.2%
3 964
 
0.2%
4 5124
 
1.2%
5 2343
 
0.5%
6 22892
5.2%
7 12087
 
2.7%
8 11602
 
2.6%
9 8919
 
2.0%
10 31839
7.2%
ValueCountFrequency (%)
21 17953
4.1%
20 11403
 
2.6%
19 16537
3.7%
18 12060
 
2.7%
17 41255
9.3%
16 23701
5.4%
15 10011
 
2.3%
14 23040
5.2%
13 4906
 
1.1%
12 12697
 
2.9%

NOC_10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing152494
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean5.1664185
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:12.093304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6504665
Coefficient of variation (CV)0.51301817
Kurtosis-1.2477242
Mean5.1664185
Median Absolute Deviation (MAD)2
Skewness-0.096944123
Sum1498685
Variance7.0249728
MonotonicityNot monotonic
2024-05-17T15:21:12.184679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 63539
14.4%
8 45740
 
10.3%
2 44303
 
10.0%
5 35356
 
8.0%
1 28064
 
6.3%
4 23293
 
5.3%
3 22203
 
5.0%
10 11576
 
2.6%
6 8357
 
1.9%
9 7651
 
1.7%
(Missing) 152494
34.5%
ValueCountFrequency (%)
1 28064
6.3%
2 44303
10.0%
3 22203
 
5.0%
4 23293
 
5.3%
5 35356
8.0%
6 8357
 
1.9%
7 63539
14.4%
8 45740
10.3%
9 7651
 
1.7%
10 11576
 
2.6%
ValueCountFrequency (%)
10 11576
 
2.6%
9 7651
 
1.7%
8 45740
10.3%
7 63539
14.4%
6 8357
 
1.9%
5 35356
8.0%
4 23293
 
5.3%
3 22203
 
5.0%
2 44303
10.0%
1 28064
6.3%

NOC_43
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)< 0.1%
Missing152494
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean22.745313
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:12.299706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median24
Q334
95-th percentile40
Maximum43
Range42
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.737997
Coefficient of variation (CV)0.5600273
Kurtosis-1.4477776
Mean22.745313
Median Absolute Deviation (MAD)11
Skewness-0.2088144
Sum6598006
Variance162.25656
MonotonicityNot monotonic
2024-05-17T15:21:12.418162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 26602
 
6.0%
35 21586
 
4.9%
33 19040
 
4.3%
36 13053
 
2.9%
20 12609
 
2.8%
9 11468
 
2.6%
2 11061
 
2.5%
8 10701
 
2.4%
7 9896
 
2.2%
32 9393
 
2.1%
Other values (33) 144673
32.7%
(Missing) 152494
34.5%
ValueCountFrequency (%)
1 852
 
0.2%
2 11061
2.5%
3 7473
1.7%
4 8678
2.0%
5 6062
1.4%
6 6176
1.4%
7 9896
2.2%
8 10701
2.4%
9 11468
2.6%
10 1380
 
0.3%
ValueCountFrequency (%)
43 1989
 
0.4%
42 6806
 
1.5%
41 2781
 
0.6%
40 4643
 
1.0%
39 3008
 
0.7%
38 8512
 
1.9%
37 2589
 
0.6%
36 13053
2.9%
35 21586
4.9%
34 26602
6.0%

YABSENT
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing421163
Missing (%)95.2%
Memory size3.4 MiB
3.0
7079 
1.0
6297 
2.0
4164 
0.0
3873 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters64239
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 7079
 
1.6%
1.0 6297
 
1.4%
2.0 4164
 
0.9%
0.0 3873
 
0.9%
(Missing) 421163
95.2%

Length

2024-05-17T15:21:12.526029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:12.642036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 7079
33.1%
1.0 6297
29.4%
2.0 4164
19.4%
0.0 3873
18.1%

Most occurring characters

ValueCountFrequency (%)
0 25286
39.4%
. 21413
33.3%
3 7079
 
11.0%
1 6297
 
9.8%
2 4164
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42826
66.7%
Other Punctuation 21413
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25286
59.0%
3 7079
 
16.5%
1 6297
 
14.7%
2 4164
 
9.7%
Other Punctuation
ValueCountFrequency (%)
. 21413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25286
39.4%
. 21413
33.3%
3 7079
 
11.0%
1 6297
 
9.8%
2 4164
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25286
39.4%
. 21413
33.3%
3 7079
 
11.0%
1 6297
 
9.8%
2 4164
 
6.5%

WKSAWAY
Real number (ℝ)

MISSING 

Distinct99
Distinct (%)0.5%
Missing421163
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean14.102928
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:12.763361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q318
95-th percentile60
Maximum99
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.754804
Coefficient of variation (CV)1.5425735
Kurtosis5.2334842
Mean14.102928
Median Absolute Deviation (MAD)2
Skewness2.3007544
Sum301986
Variance473.27149
MonotonicityNot monotonic
2024-05-17T15:21:12.893475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7087
 
1.6%
2 2454
 
0.6%
3 1328
 
0.3%
4 942
 
0.2%
99 575
 
0.1%
6 515
 
0.1%
5 508
 
0.1%
8 507
 
0.1%
12 409
 
0.1%
52 381
 
0.1%
Other values (89) 6707
 
1.5%
(Missing) 421163
95.2%
ValueCountFrequency (%)
1 7087
1.6%
2 2454
 
0.6%
3 1328
 
0.3%
4 942
 
0.2%
5 508
 
0.1%
6 515
 
0.1%
7 283
 
0.1%
8 507
 
0.1%
9 219
 
< 0.1%
10 363
 
0.1%
ValueCountFrequency (%)
99 575
0.1%
98 2
 
< 0.1%
97 1
 
< 0.1%
96 5
 
< 0.1%
95 6
 
< 0.1%
94 4
 
< 0.1%
93 5
 
< 0.1%
92 7
 
< 0.1%
91 2
 
< 0.1%
90 8
 
< 0.1%

PAYAWAY
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing423510
Missing (%)95.7%
Memory size3.4 MiB
2.0
10856 
1.0
8210 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57198
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 10856
 
2.5%
1.0 8210
 
1.9%
(Missing) 423510
95.7%

Length

2024-05-17T15:21:13.005343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:13.111148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 10856
56.9%
1.0 8210
43.1%

Most occurring characters

ValueCountFrequency (%)
. 19066
33.3%
0 19066
33.3%
2 10856
19.0%
1 8210
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38132
66.7%
Other Punctuation 19066
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19066
50.0%
2 10856
28.5%
1 8210
21.5%
Other Punctuation
ValueCountFrequency (%)
. 19066
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 19066
33.3%
0 19066
33.3%
2 10856
19.0%
1 8210
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 19066
33.3%
0 19066
33.3%
2 10856
19.0%
1 8210
14.4%

UHRSMAIN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct379
Distinct (%)0.1%
Missing184245
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean354.56949
Minimum1
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:13.219374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1320
median390
Q3400
95-th percentile500
Maximum990
Range989
Interquartile range (IQR)80

Descriptive statistics

Standard deviation117.46765
Coefficient of variation (CV)0.33129656
Kurtosis3.0051763
Mean354.56949
Median Absolute Deviation (MAD)15
Skewness-0.16922213
Sum91596290
Variance13798.649
MonotonicityNot monotonic
2024-05-17T15:21:13.349672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 99635
22.5%
375 29469
 
6.7%
350 22261
 
5.0%
300 10838
 
2.4%
200 8640
 
2.0%
500 6643
 
1.5%
320 4879
 
1.1%
250 4762
 
1.1%
150 4558
 
1.0%
450 4500
 
1.0%
Other values (369) 62146
 
14.0%
(Missing) 184245
41.6%
ValueCountFrequency (%)
1 5
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 24
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 284
0.1%
12 2
 
< 0.1%
15 25
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
990 152
< 0.1%
980 19
 
< 0.1%
970 2
 
< 0.1%
960 30
 
< 0.1%
950 7
 
< 0.1%
940 1
 
< 0.1%
920 3
 
< 0.1%
910 10
 
< 0.1%
900 73
< 0.1%
880 16
 
< 0.1%

AHRSMAIN
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct561
Distinct (%)0.2%
Missing184245
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean322.93957
Minimum0
Maximum990
Zeros21570
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:13.479560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median375
Q3400
95-th percentile525
Maximum990
Range990
Interquartile range (IQR)160

Descriptive statistics

Standard deviation158.92171
Coefficient of variation (CV)0.49210975
Kurtosis0.6077742
Mean322.93957
Median Absolute Deviation (MAD)55
Skewness-0.35086597
Sum83425301
Variance25256.11
MonotonicityNot monotonic
2024-05-17T15:21:13.612969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 66877
 
15.1%
0 21570
 
4.9%
375 17906
 
4.0%
350 14416
 
3.3%
300 9833
 
2.2%
320 8249
 
1.9%
500 7319
 
1.7%
200 6961
 
1.6%
450 5648
 
1.3%
240 4720
 
1.1%
Other values (551) 94832
21.4%
(Missing) 184245
41.6%
ValueCountFrequency (%)
0 21570
4.9%
1 2
 
< 0.1%
5 27
 
< 0.1%
7 1
 
< 0.1%
10 315
 
0.1%
12 4
 
< 0.1%
15 44
 
< 0.1%
20 517
 
0.1%
21 4
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
990 217
< 0.1%
980 26
 
< 0.1%
975 3
 
< 0.1%
970 4
 
< 0.1%
960 37
 
< 0.1%
950 13
 
< 0.1%
940 7
 
< 0.1%
935 2
 
< 0.1%
930 1
 
< 0.1%
920 15
 
< 0.1%

FTPTMAIN
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing184245
Missing (%)41.6%
Memory size3.4 MiB
1.0
209593 
2.0
48738 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters774993
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 209593
47.4%
2.0 48738
 
11.0%
(Missing) 184245
41.6%

Length

2024-05-17T15:21:13.730074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:13.831454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 209593
81.1%
2.0 48738
 
18.9%

Most occurring characters

ValueCountFrequency (%)
. 258331
33.3%
0 258331
33.3%
1 209593
27.0%
2 48738
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 516662
66.7%
Other Punctuation 258331
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258331
50.0%
1 209593
40.6%
2 48738
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 258331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 774993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 258331
33.3%
0 258331
33.3%
1 209593
27.0%
2 48738
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 774993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 258331
33.3%
0 258331
33.3%
1 209593
27.0%
2 48738
 
6.3%

UTOTHRS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct435
Distinct (%)0.2%
Missing184245
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean362.30302
Minimum1
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:13.936908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1340
median400
Q3400
95-th percentile540
Maximum990
Range989
Interquartile range (IQR)60

Descriptive statistics

Standard deviation122.80596
Coefficient of variation (CV)0.33895925
Kurtosis3.1538863
Mean362.30302
Median Absolute Deviation (MAD)25
Skewness0.076628678
Sum93594101
Variance15081.304
MonotonicityNot monotonic
2024-05-17T15:21:14.067042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 96364
21.8%
375 28191
 
6.4%
350 21268
 
4.8%
300 10148
 
2.3%
200 7968
 
1.8%
500 7210
 
1.6%
450 4984
 
1.1%
320 4692
 
1.1%
250 4413
 
1.0%
150 4208
 
1.0%
Other values (425) 68885
 
15.6%
(Missing) 184245
41.6%
ValueCountFrequency (%)
1 5
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 24
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 269
0.1%
12 2
 
< 0.1%
15 24
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
990 308
0.1%
980 19
 
< 0.1%
975 1
 
< 0.1%
970 6
 
< 0.1%
960 30
 
< 0.1%
950 23
 
< 0.1%
940 4
 
< 0.1%
930 8
 
< 0.1%
920 5
 
< 0.1%
910 11
 
< 0.1%

ATOTHRS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct580
Distinct (%)0.2%
Missing184245
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean329.84996
Minimum0
Maximum990
Zeros21413
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:14.198085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median375
Q3400
95-th percentile550
Maximum990
Range990
Interquartile range (IQR)160

Descriptive statistics

Standard deviation163.36818
Coefficient of variation (CV)0.49528028
Kurtosis0.72539995
Mean329.84996
Median Absolute Deviation (MAD)55
Skewness-0.25758995
Sum85210470
Variance26689.163
MonotonicityNot monotonic
2024-05-17T15:21:14.330722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 64875
 
14.7%
0 21413
 
4.8%
375 17148
 
3.9%
350 13849
 
3.1%
300 9310
 
2.1%
320 7971
 
1.8%
500 7604
 
1.7%
200 6543
 
1.5%
450 5856
 
1.3%
240 4460
 
1.0%
Other values (570) 99302
22.4%
(Missing) 184245
41.6%
ValueCountFrequency (%)
0 21413
4.8%
1 2
 
< 0.1%
5 29
 
< 0.1%
7 1
 
< 0.1%
10 293
 
0.1%
12 4
 
< 0.1%
15 42
 
< 0.1%
20 495
 
0.1%
21 4
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
990 363
0.1%
980 27
 
< 0.1%
975 4
 
< 0.1%
970 8
 
< 0.1%
960 43
 
< 0.1%
950 28
 
< 0.1%
940 13
 
< 0.1%
935 1
 
< 0.1%
930 1
 
< 0.1%
920 20
 
< 0.1%

HRSAWAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct207
Distinct (%)0.1%
Missing236312
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean14.310398
Minimum0
Maximum990
Zeros178820
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:14.464313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile80
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.67746
Coefficient of variation (CV)3.2617862
Kurtosis26.655018
Mean14.310398
Median Absolute Deviation (MAD)0
Skewness4.5492512
Sum2951720
Variance2178.7852
MonotonicityNot monotonic
2024-05-17T15:21:14.596764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178820
40.4%
80 6595
 
1.5%
160 2106
 
0.5%
75 2105
 
0.5%
70 1592
 
0.4%
40 1496
 
0.3%
20 1090
 
0.2%
240 939
 
0.2%
150 879
 
0.2%
100 875
 
0.2%
Other values (197) 9767
 
2.2%
(Missing) 236312
53.4%
ValueCountFrequency (%)
0 178820
40.4%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 2
 
< 0.1%
5 92
 
< 0.1%
7 9
 
< 0.1%
8 2
 
< 0.1%
10 589
 
0.1%
11 1
 
< 0.1%
12 8
 
< 0.1%
ValueCountFrequency (%)
990 1
 
< 0.1%
960 1
 
< 0.1%
720 3
 
< 0.1%
700 1
 
< 0.1%
650 1
 
< 0.1%
600 13
< 0.1%
580 1
 
< 0.1%
560 3
 
< 0.1%
520 1
 
< 0.1%
500 7
< 0.1%

YAWAY
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing415132
Missing (%)93.8%
Memory size3.4 MiB
1.0
10283 
3.0
7787 
2.0
5523 
0.0
3270 
4.0
 
581

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82332
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10283
 
2.3%
3.0 7787
 
1.8%
2.0 5523
 
1.2%
0.0 3270
 
0.7%
4.0 581
 
0.1%
(Missing) 415132
93.8%

Length

2024-05-17T15:21:14.713370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:14.826087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10283
37.5%
3.0 7787
28.4%
2.0 5523
20.1%
0.0 3270
 
11.9%
4.0 581
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 30714
37.3%
. 27444
33.3%
1 10283
 
12.5%
3 7787
 
9.5%
2 5523
 
6.7%
4 581
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54888
66.7%
Other Punctuation 27444
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30714
56.0%
1 10283
 
18.7%
3 7787
 
14.2%
2 5523
 
10.1%
4 581
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 27444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30714
37.3%
. 27444
33.3%
1 10283
 
12.5%
3 7787
 
9.5%
2 5523
 
6.7%
4 581
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30714
37.3%
. 27444
33.3%
1 10283
 
12.5%
3 7787
 
9.5%
2 5523
 
6.7%
4 581
 
0.7%

PAIDOT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct208
Distinct (%)0.1%
Missing236312
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean7.7209789
Minimum0
Maximum990
Zeros187593
Zeros (%)42.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:14.947632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.819115
Coefficient of variation (CV)4.5096762
Kurtosis71.853871
Mean7.7209789
Median Absolute Deviation (MAD)0
Skewness7.1939184
Sum1592560
Variance1212.3707
MonotonicityNot monotonic
2024-05-17T15:21:15.082304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 187593
42.4%
20 1731
 
0.4%
40 1667
 
0.4%
80 1640
 
0.4%
100 1611
 
0.4%
50 1454
 
0.3%
10 1223
 
0.3%
30 1143
 
0.3%
120 903
 
0.2%
60 890
 
0.2%
Other values (198) 6409
 
1.4%
(Missing) 236312
53.4%
ValueCountFrequency (%)
0 187593
42.4%
1 7
 
< 0.1%
2 24
 
< 0.1%
3 32
 
< 0.1%
4 6
 
< 0.1%
5 330
 
0.1%
6 3
 
< 0.1%
7 20
 
< 0.1%
8 12
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
990 1
 
< 0.1%
820 1
 
< 0.1%
800 1
 
< 0.1%
750 1
 
< 0.1%
730 1
 
< 0.1%
720 3
< 0.1%
700 5
< 0.1%
695 1
 
< 0.1%
680 1
 
< 0.1%
625 1
 
< 0.1%

UNPAIDOT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct120
Distinct (%)0.1%
Missing236312
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean6.5365842
Minimum0
Maximum990
Zeros188588
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:15.217348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.90528
Coefficient of variation (CV)4.4220773
Kurtosis97.834219
Mean6.5365842
Median Absolute Deviation (MAD)0
Skewness7.5065226
Sum1348262
Variance835.51524
MonotonicityNot monotonic
2024-05-17T15:21:15.351983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 188588
42.6%
50 2948
 
0.7%
100 2828
 
0.6%
20 1780
 
0.4%
40 1421
 
0.3%
30 1298
 
0.3%
10 969
 
0.2%
150 962
 
0.2%
60 882
 
0.2%
80 881
 
0.2%
Other values (110) 3707
 
0.8%
(Missing) 236312
53.4%
ValueCountFrequency (%)
0 188588
42.6%
2 15
 
< 0.1%
3 9
 
< 0.1%
4 2
 
< 0.1%
5 205
 
< 0.1%
6 3
 
< 0.1%
7 10
 
< 0.1%
8 8
 
< 0.1%
10 969
 
0.2%
12 6
 
< 0.1%
ValueCountFrequency (%)
990 3
 
< 0.1%
980 1
 
< 0.1%
880 1
 
< 0.1%
800 3
 
< 0.1%
750 2
 
< 0.1%
650 1
 
< 0.1%
630 1
 
< 0.1%
600 8
< 0.1%
560 1
 
< 0.1%
550 2
 
< 0.1%

XTRAHRS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct232
Distinct (%)0.1%
Missing236312
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean14.257563
Minimum0
Maximum990
Zeros170981
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:15.488326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44.704272
Coefficient of variation (CV)3.1354778
Kurtosis42.22781
Mean14.257563
Median Absolute Deviation (MAD)0
Skewness5.2395814
Sum2940822
Variance1998.472
MonotonicityNot monotonic
2024-05-17T15:21:15.624037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 170981
38.6%
100 4367
 
1.0%
50 4209
 
1.0%
20 3259
 
0.7%
40 2954
 
0.7%
80 2487
 
0.6%
30 2250
 
0.5%
10 1955
 
0.4%
60 1754
 
0.4%
200 1453
 
0.3%
Other values (222) 10595
 
2.4%
(Missing) 236312
53.4%
ValueCountFrequency (%)
0 170981
38.6%
1 5
 
< 0.1%
2 32
 
< 0.1%
3 40
 
< 0.1%
4 7
 
< 0.1%
5 466
 
0.1%
6 2
 
< 0.1%
7 26
 
< 0.1%
8 19
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
990 4
< 0.1%
980 1
 
< 0.1%
880 1
 
< 0.1%
840 1
 
< 0.1%
820 2
 
< 0.1%
800 4
< 0.1%
750 3
< 0.1%
730 1
 
< 0.1%
720 4
< 0.1%
700 6
< 0.1%

WHYPT
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing393838
Missing (%)89.0%
Infinite0
Infinite (%)0.0%
Mean4.1078214
Minimum0
Maximum7
Zeros2952
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:15.732400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7762387
Coefficient of variation (CV)0.43240407
Kurtosis0.11897954
Mean4.1078214
Median Absolute Deviation (MAD)1
Skewness-0.63687555
Sum200207
Variance3.1550239
MonotonicityNot monotonic
2024-05-17T15:21:15.827082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 14969
 
3.4%
5 14552
 
3.3%
7 4596
 
1.0%
2 3721
 
0.8%
6 2987
 
0.7%
0 2952
 
0.7%
3 2537
 
0.6%
1 2424
 
0.5%
(Missing) 393838
89.0%
ValueCountFrequency (%)
0 2952
 
0.7%
1 2424
 
0.5%
2 3721
 
0.8%
3 2537
 
0.6%
4 14969
3.4%
5 14552
3.3%
6 2987
 
0.7%
7 4596
 
1.0%
ValueCountFrequency (%)
7 4596
 
1.0%
6 2987
 
0.7%
5 14552
3.3%
4 14969
3.4%
3 2537
 
0.6%
2 3721
 
0.8%
1 2424
 
0.5%
0 2952
 
0.7%

TENURE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct240
Distinct (%)0.1%
Missing184245
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean94.711494
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:15.949457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q121
median64
Q3165
95-th percentile240
Maximum240
Range239
Interquartile range (IQR)144

Descriptive statistics

Standard deviation84.343062
Coefficient of variation (CV)0.89052615
Kurtosis-1.0843687
Mean94.711494
Median Absolute Deviation (MAD)52
Skewness0.63995976
Sum24466915
Variance7113.7522
MonotonicityNot monotonic
2024-05-17T15:21:16.081084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 37010
 
8.4%
6 4236
 
1.0%
7 4047
 
0.9%
8 3956
 
0.9%
5 3928
 
0.9%
4 3599
 
0.8%
2 3549
 
0.8%
3 3499
 
0.8%
9 3491
 
0.8%
10 3296
 
0.7%
Other values (230) 187720
42.4%
(Missing) 184245
41.6%
ValueCountFrequency (%)
1 2784
0.6%
2 3549
0.8%
3 3499
0.8%
4 3599
0.8%
5 3928
0.9%
6 4236
1.0%
7 4047
0.9%
8 3956
0.9%
9 3491
0.8%
10 3296
0.7%
ValueCountFrequency (%)
240 37010
8.4%
239 316
 
0.1%
238 306
 
0.1%
237 304
 
0.1%
236 350
 
0.1%
235 321
 
0.1%
234 305
 
0.1%
233 247
 
0.1%
232 250
 
0.1%
231 239
 
0.1%

PREVTEN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct240
Distinct (%)0.8%
Missing410825
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean58.6385
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:16.507907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median17
Q375
95-th percentile240
Maximum240
Range239
Interquartile range (IQR)70

Descriptive statistics

Standard deviation80.168286
Coefficient of variation (CV)1.3671613
Kurtosis0.52987434
Mean58.6385
Median Absolute Deviation (MAD)14
Skewness1.4324672
Sum1861831
Variance6426.954
MonotonicityNot monotonic
2024-05-17T15:21:16.638213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 3490
 
0.8%
4 2286
 
0.5%
3 2014
 
0.5%
1 1709
 
0.4%
2 1702
 
0.4%
5 1199
 
0.3%
6 1023
 
0.2%
7 947
 
0.2%
8 934
 
0.2%
9 584
 
0.1%
Other values (230) 15863
 
3.6%
(Missing) 410825
92.8%
ValueCountFrequency (%)
1 1709
0.4%
2 1702
0.4%
3 2014
0.5%
4 2286
0.5%
5 1199
0.3%
6 1023
0.2%
7 947
0.2%
8 934
0.2%
9 584
 
0.1%
10 467
 
0.1%
ValueCountFrequency (%)
240 3490
0.8%
239 24
 
< 0.1%
238 13
 
< 0.1%
237 17
 
< 0.1%
236 20
 
< 0.1%
235 22
 
< 0.1%
234 33
 
< 0.1%
233 11
 
< 0.1%
232 18
 
< 0.1%
231 19
 
< 0.1%

HRLYEARN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5838
Distinct (%)2.6%
Missing218784
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean3433.6227
Minimum577
Maximum21635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:16.767705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum577
5-th percentile1550
Q12100
median2933
Q34300
95-th percentile6785.9
Maximum21635
Range21058
Interquartile range (IQR)2200

Descriptive statistics

Standard deviation1849.8759
Coefficient of variation (CV)0.53875341
Kurtosis7.5171409
Mean3433.6227
Median Absolute Deviation (MAD)973
Skewness2.064211
Sum7.6841728 × 108
Variance3422041
MonotonicityNot monotonic
2024-05-17T15:21:16.890641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 7211
 
1.6%
2500 6889
 
1.6%
1800 4712
 
1.1%
3000 4701
 
1.1%
2200 4298
 
1.0%
1700 4231
 
1.0%
1500 3958
 
0.9%
2100 3576
 
0.8%
2300 3534
 
0.8%
1600 3480
 
0.8%
Other values (5828) 177202
40.0%
(Missing) 218784
49.4%
ValueCountFrequency (%)
577 1
 
< 0.1%
582 1
 
< 0.1%
667 1
 
< 0.1%
673 1
 
< 0.1%
692 4
< 0.1%
703 1
 
< 0.1%
714 1
 
< 0.1%
721 5
< 0.1%
722 1
 
< 0.1%
735 1
 
< 0.1%
ValueCountFrequency (%)
21635 1
 
< 0.1%
21410 1
 
< 0.1%
20833 1
 
< 0.1%
20673 1
 
< 0.1%
20568 1
 
< 0.1%
20529 1
 
< 0.1%
20513 3
 
< 0.1%
20000 1
 
< 0.1%
19780 2
 
< 0.1%
19231 29
< 0.1%

UNION
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing218784
Missing (%)49.4%
Memory size3.4 MiB
3.0
149896 
1.0
69402 
2.0
 
4494

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 149896
33.9%
1.0 69402
 
15.7%
2.0 4494
 
1.0%
(Missing) 218784
49.4%

Length

2024-05-17T15:21:17.000873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:17.113661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 149896
67.0%
1.0 69402
31.0%
2.0 4494
 
2.0%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
3 149896
22.3%
1 69402
 
10.3%
2 4494
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
3 149896
33.5%
1 69402
 
15.5%
2 4494
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
3 149896
22.3%
1 69402
 
10.3%
2 4494
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
3 149896
22.3%
1 69402
 
10.3%
2 4494
 
0.7%

PERMTEMP
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218784
Missing (%)49.4%
Memory size3.4 MiB
1.0
200416 
3.0
 
13074
4.0
 
7555
2.0
 
2747

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 200416
45.3%
3.0 13074
 
3.0%
4.0 7555
 
1.7%
2.0 2747
 
0.6%
(Missing) 218784
49.4%

Length

2024-05-17T15:21:17.208770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:17.322341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 200416
89.6%
3.0 13074
 
5.8%
4.0 7555
 
3.4%
2.0 2747
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 200416
29.9%
3 13074
 
1.9%
4 7555
 
1.1%
2 2747
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
1 200416
44.8%
3 13074
 
2.9%
4 7555
 
1.7%
2 2747
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 200416
29.9%
3 13074
 
1.9%
4 7555
 
1.1%
2 2747
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 200416
29.9%
3 13074
 
1.9%
4 7555
 
1.1%
2 2747
 
0.4%

ESTSIZE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218784
Missing (%)49.4%
Memory size3.4 MiB
2.0
74001 
1.0
67963 
3.0
46275 
4.0
35553 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 74001
 
16.7%
1.0 67963
 
15.4%
3.0 46275
 
10.5%
4.0 35553
 
8.0%
(Missing) 218784
49.4%

Length

2024-05-17T15:21:17.418650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:17.534865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 74001
33.1%
1.0 67963
30.4%
3.0 46275
20.7%
4.0 35553
15.9%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 74001
 
11.0%
1 67963
 
10.1%
3 46275
 
6.9%
4 35553
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
2 74001
 
16.5%
1 67963
 
15.2%
3 46275
 
10.3%
4 35553
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 74001
 
11.0%
1 67963
 
10.1%
3 46275
 
6.9%
4 35553
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 74001
 
11.0%
1 67963
 
10.1%
3 46275
 
6.9%
4 35553
 
5.3%

FIRMSIZE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218784
Missing (%)49.4%
Memory size3.4 MiB
4.0
113823 
1.0
37302 
2.0
36718 
3.0
35949 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 113823
25.7%
1.0 37302
 
8.4%
2.0 36718
 
8.3%
3.0 35949
 
8.1%
(Missing) 218784
49.4%

Length

2024-05-17T15:21:17.638922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:17.756188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 113823
50.9%
1.0 37302
 
16.7%
2.0 36718
 
16.4%
3.0 35949
 
16.1%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
4 113823
17.0%
1 37302
 
5.6%
2 36718
 
5.5%
3 35949
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
4 113823
25.4%
1 37302
 
8.3%
2 36718
 
8.2%
3 35949
 
8.0%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
4 113823
17.0%
1 37302
 
5.6%
2 36718
 
5.5%
3 35949
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
4 113823
17.0%
1 37302
 
5.6%
2 36718
 
5.5%
3 35949
 
5.4%

DURUNEMP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)0.6%
Missing426231
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean16.453166
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:17.877934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median10
Q320
95-th percentile52
Maximum99
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.353499
Coefficient of variation (CV)1.1762781
Kurtosis5.8473124
Mean16.453166
Median Absolute Deviation (MAD)6
Skewness2.3046171
Sum268927
Variance374.5579
MonotonicityNot monotonic
2024-05-17T15:21:18.010048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2085
 
0.5%
3 1192
 
0.3%
8 1173
 
0.3%
2 1165
 
0.3%
12 1068
 
0.2%
6 765
 
0.2%
16 751
 
0.2%
1 744
 
0.2%
10 687
 
0.2%
20 687
 
0.2%
Other values (85) 6028
 
1.4%
(Missing) 426231
96.3%
ValueCountFrequency (%)
1 744
 
0.2%
2 1165
0.3%
3 1192
0.3%
4 2085
0.5%
5 352
 
0.1%
6 765
 
0.2%
7 416
 
0.1%
8 1173
0.3%
9 182
 
< 0.1%
10 687
 
0.2%
ValueCountFrequency (%)
99 322
0.1%
98 2
 
< 0.1%
97 1
 
< 0.1%
96 5
 
< 0.1%
95 1
 
< 0.1%
94 2
 
< 0.1%
92 11
 
< 0.1%
90 5
 
< 0.1%
89 2
 
< 0.1%
88 3
 
< 0.1%

FLOWUNEM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing425429
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean4.3762174
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:18.116494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median5
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1303605
Coefficient of variation (CV)0.48680408
Kurtosis-1.4352849
Mean4.3762174
Median Absolute Deviation (MAD)2
Skewness-0.0053892116
Sum75039
Variance4.5384359
MonotonicityNot monotonic
2024-05-17T15:21:18.206752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 4903
 
1.1%
6 3389
 
0.8%
7 2587
 
0.6%
5 2269
 
0.5%
3 1293
 
0.3%
4 1082
 
0.2%
1 822
 
0.2%
8 802
 
0.2%
(Missing) 425429
96.1%
ValueCountFrequency (%)
1 822
 
0.2%
2 4903
1.1%
3 1293
 
0.3%
4 1082
 
0.2%
5 2269
0.5%
6 3389
0.8%
7 2587
0.6%
8 802
 
0.2%
ValueCountFrequency (%)
8 802
 
0.2%
7 2587
0.6%
6 3389
0.8%
5 2269
0.5%
4 1082
 
0.2%
3 1293
 
0.3%
2 4903
1.1%
1 822
 
0.2%

UNEMFTPT
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing425429
Missing (%)96.1%
Memory size3.4 MiB
1.0
12037 
2.0
4308 
3.0
 
802

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51441
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12037
 
2.7%
2.0 4308
 
1.0%
3.0 802
 
0.2%
(Missing) 425429
96.1%

Length

2024-05-17T15:21:18.316485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:18.430057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12037
70.2%
2.0 4308
 
25.1%
3.0 802
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 17147
33.3%
0 17147
33.3%
1 12037
23.4%
2 4308
 
8.4%
3 802
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34294
66.7%
Other Punctuation 17147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17147
50.0%
1 12037
35.1%
2 4308
 
12.6%
3 802
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 17147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17147
33.3%
0 17147
33.3%
1 12037
23.4%
2 4308
 
8.4%
3 802
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17147
33.3%
0 17147
33.3%
1 12037
23.4%
2 4308
 
8.4%
3 802
 
1.6%

WHYLEFTO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing410825
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean3.319423
Minimum0
Maximum5
Zeros3105
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:18.515289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4084158
Coefficient of variation (CV)0.42429536
Kurtosis0.65167799
Mean3.319423
Median Absolute Deviation (MAD)1
Skewness-1.2519133
Sum105395
Variance1.983635
MonotonicityNot monotonic
2024-05-17T15:21:18.609126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 15848
 
3.6%
3 6217
 
1.4%
5 3868
 
0.9%
0 3105
 
0.7%
1 1414
 
0.3%
2 1299
 
0.3%
(Missing) 410825
92.8%
ValueCountFrequency (%)
0 3105
 
0.7%
1 1414
 
0.3%
2 1299
 
0.3%
3 6217
 
1.4%
4 15848
3.6%
5 3868
 
0.9%
ValueCountFrequency (%)
5 3868
 
0.9%
4 15848
3.6%
3 6217
 
1.4%
2 1299
 
0.3%
1 1414
 
0.3%
0 3105
 
0.7%

WHYLEFTN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)< 0.1%
Missing410825
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean7.5882649
Minimum0
Maximum13
Zeros1175
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:18.703951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median8
Q310
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3710224
Coefficient of variation (CV)0.44424152
Kurtosis-0.46681837
Mean7.5882649
Median Absolute Deviation (MAD)2
Skewness-0.40716866
Sum240935
Variance11.363792
MonotonicityNot monotonic
2024-05-17T15:21:18.807439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5 6217
 
1.4%
9 6016
 
1.4%
10 4066
 
0.9%
7 3868
 
0.9%
12 3760
 
0.8%
6 1660
 
0.4%
13 1522
 
0.3%
1 1414
 
0.3%
0 1175
 
0.3%
4 657
 
0.1%
Other values (4) 1396
 
0.3%
(Missing) 410825
92.8%
ValueCountFrequency (%)
0 1175
 
0.3%
1 1414
 
0.3%
2 348
 
0.1%
3 294
 
0.1%
4 657
 
0.1%
5 6217
1.4%
6 1660
 
0.4%
7 3868
0.9%
8 270
 
0.1%
9 6016
1.4%
ValueCountFrequency (%)
13 1522
 
0.3%
12 3760
0.8%
11 484
 
0.1%
10 4066
0.9%
9 6016
1.4%
8 270
 
0.1%
7 3868
0.9%
6 1660
 
0.4%
5 6217
1.4%
4 657
 
0.1%

DURJLESS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct239
Distinct (%)0.2%
Missing288071
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean107.74683
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:18.928306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q121
median86
Q3196
95-th percentile240
Maximum240
Range239
Interquartile range (IQR)175

Descriptive statistics

Standard deviation88.871668
Coefficient of variation (CV)0.82481932
Kurtosis-1.4084006
Mean107.74683
Median Absolute Deviation (MAD)76
Skewness0.34492372
Sum16647424
Variance7898.1733
MonotonicityNot monotonic
2024-05-17T15:21:19.060432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 29473
 
6.7%
2 3445
 
0.8%
6 3439
 
0.8%
4 3227
 
0.7%
5 3208
 
0.7%
3 3185
 
0.7%
7 3131
 
0.7%
8 2908
 
0.7%
1 2494
 
0.6%
9 2449
 
0.6%
Other values (229) 97546
 
22.0%
(Missing) 288071
65.1%
ValueCountFrequency (%)
1 2494
0.6%
2 3445
0.8%
3 3185
0.7%
4 3227
0.7%
5 3208
0.7%
6 3439
0.8%
7 3131
0.7%
8 2908
0.7%
9 2449
0.6%
10 1386
0.3%
ValueCountFrequency (%)
240 29473
6.7%
239 277
 
0.1%
238 265
 
0.1%
237 268
 
0.1%
236 281
 
0.1%
235 190
 
< 0.1%
234 152
 
< 0.1%
233 156
 
< 0.1%
232 177
 
< 0.1%
231 152
 
< 0.1%

AVAILABL
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing422834
Missing (%)95.5%
Memory size3.4 MiB
2.0
18136 
1.0
 
1606

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters59226
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 18136
 
4.1%
1.0 1606
 
0.4%
(Missing) 422834
95.5%

Length

2024-05-17T15:21:19.173306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:19.280481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 18136
91.9%
1.0 1606
 
8.1%

Most occurring characters

ValueCountFrequency (%)
. 19742
33.3%
0 19742
33.3%
2 18136
30.6%
1 1606
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39484
66.7%
Other Punctuation 19742
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19742
50.0%
2 18136
45.9%
1 1606
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 19742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59226
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 19742
33.3%
0 19742
33.3%
2 18136
30.6%
1 1606
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 19742
33.3%
0 19742
33.3%
2 18136
30.6%
1 1606
 
2.7%

LKPUBAG
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing440335
Missing (%)99.5%
Memory size3.4 MiB
1.0
2241 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6723
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2241
 
0.5%
(Missing) 440335
99.5%

Length

2024-05-17T15:21:19.369533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:19.474203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2241
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2241
33.3%
. 2241
33.3%
0 2241
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4482
66.7%
Other Punctuation 2241
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2241
50.0%
0 2241
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6723
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2241
33.3%
. 2241
33.3%
0 2241
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2241
33.3%
. 2241
33.3%
0 2241
33.3%

LKEMPLOY
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing434674
Missing (%)98.2%
Memory size3.4 MiB
1.0
7902 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23706
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7902
 
1.8%
(Missing) 434674
98.2%

Length

2024-05-17T15:21:19.558558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:19.658910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7902
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7902
33.3%
. 7902
33.3%
0 7902
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15804
66.7%
Other Punctuation 7902
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7902
50.0%
0 7902
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23706
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7902
33.3%
. 7902
33.3%
0 7902
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7902
33.3%
. 7902
33.3%
0 7902
33.3%

LKRELS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing437169
Missing (%)98.8%
Memory size3.4 MiB
1.0
5407 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16221
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5407
 
1.2%
(Missing) 437169
98.8%

Length

2024-05-17T15:21:19.745163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:19.841658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5407
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5407
33.3%
. 5407
33.3%
0 5407
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10814
66.7%
Other Punctuation 5407
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5407
50.0%
0 5407
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16221
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5407
33.3%
. 5407
33.3%
0 5407
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5407
33.3%
. 5407
33.3%
0 5407
33.3%

LKATADS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing430293
Missing (%)97.2%
Memory size3.4 MiB
1.0
12283 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36849
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12283
 
2.8%
(Missing) 430293
97.2%

Length

2024-05-17T15:21:19.924709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:20.025287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12283
100.0%

Most occurring characters

ValueCountFrequency (%)
1 12283
33.3%
. 12283
33.3%
0 12283
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24566
66.7%
Other Punctuation 12283
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12283
50.0%
0 12283
50.0%
Other Punctuation
ValueCountFrequency (%)
. 12283
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12283
33.3%
. 12283
33.3%
0 12283
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12283
33.3%
. 12283
33.3%
0 12283
33.3%

LKANSADS
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing435503
Missing (%)98.4%
Memory size3.4 MiB
1.0
7073 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21219
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7073
 
1.6%
(Missing) 435503
98.4%

Length

2024-05-17T15:21:20.111661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:20.215792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7073
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7073
33.3%
. 7073
33.3%
0 7073
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14146
66.7%
Other Punctuation 7073
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7073
50.0%
0 7073
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7073
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7073
33.3%
. 7073
33.3%
0 7073
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7073
33.3%
. 7073
33.3%
0 7073
33.3%

LKOTHERN
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing438545
Missing (%)99.1%
Memory size3.4 MiB
1.0
4031 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12093
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4031
 
0.9%
(Missing) 438545
99.1%

Length

2024-05-17T15:21:20.300022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:20.402964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4031
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4031
33.3%
. 4031
33.3%
0 4031
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8062
66.7%
Other Punctuation 4031
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4031
50.0%
0 4031
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12093
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4031
33.3%
. 4031
33.3%
0 4031
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4031
33.3%
. 4031
33.3%
0 4031
33.3%

PRIORACT
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing427053
Missing (%)96.5%
Memory size3.4 MiB
1.0
7278 
3.0
3453 
2.0
2931 
0.0
1861 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters46569
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7278
 
1.6%
3.0 3453
 
0.8%
2.0 2931
 
0.7%
0.0 1861
 
0.4%
(Missing) 427053
96.5%

Length

2024-05-17T15:21:20.489700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:20.606829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7278
46.9%
3.0 3453
22.2%
2.0 2931
18.9%
0.0 1861
 
12.0%

Most occurring characters

ValueCountFrequency (%)
0 17384
37.3%
. 15523
33.3%
1 7278
15.6%
3 3453
 
7.4%
2 2931
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31046
66.7%
Other Punctuation 15523
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17384
56.0%
1 7278
23.4%
3 3453
 
11.1%
2 2931
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 15523
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46569
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17384
37.3%
. 15523
33.3%
1 7278
15.6%
3 3453
 
7.4%
2 2931
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17384
37.3%
. 15523
33.3%
1 7278
15.6%
3 3453
 
7.4%
2 2931
 
6.3%

YNOLOOK
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)0.1%
Missing436914
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean2.4784528
Minimum0
Maximum6
Zeros955
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:20.699336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8998448
Coefficient of variation (CV)0.76654466
Kurtosis-1.3231813
Mean2.4784528
Median Absolute Deviation (MAD)2
Skewness0.22652333
Sum14033
Variance3.6094102
MonotonicityNot monotonic
2024-05-17T15:21:20.789261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 1555
 
0.4%
4 1292
 
0.3%
0 955
 
0.2%
5 662
 
0.1%
2 500
 
0.1%
3 396
 
0.1%
6 302
 
0.1%
(Missing) 436914
98.7%
ValueCountFrequency (%)
0 955
0.2%
1 1555
0.4%
2 500
 
0.1%
3 396
 
0.1%
4 1292
0.3%
5 662
0.1%
6 302
 
0.1%
ValueCountFrequency (%)
6 302
 
0.1%
5 662
0.1%
4 1292
0.3%
3 396
 
0.1%
2 500
 
0.1%
1 1555
0.4%
0 955
0.2%

TLOLOOK
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing441754
Missing (%)99.8%
Memory size3.4 MiB
2.0
524 
1.0
298 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2466
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 524
 
0.1%
1.0 298
 
0.1%
(Missing) 441754
99.8%

Length

2024-05-17T15:21:20.891710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:20.999033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 524
63.7%
1.0 298
36.3%

Most occurring characters

ValueCountFrequency (%)
. 822
33.3%
0 822
33.3%
2 524
21.2%
1 298
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1644
66.7%
Other Punctuation 822
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 822
50.0%
2 524
31.9%
1 298
 
18.1%
Other Punctuation
ValueCountFrequency (%)
. 822
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 822
33.3%
0 822
33.3%
2 524
21.2%
1 298
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 822
33.3%
0 822
33.3%
2 524
21.2%
1 298
 
12.1%

SCHOOLN
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing112181
Missing (%)25.3%
Memory size3.4 MiB
1.0
281283 
2.0
42299 
3.0
 
6813

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters991185
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 281283
63.6%
2.0 42299
 
9.6%
3.0 6813
 
1.5%
(Missing) 112181
 
25.3%

Length

2024-05-17T15:21:21.092154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:21.201517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 281283
85.1%
2.0 42299
 
12.8%
3.0 6813
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 330395
33.3%
0 330395
33.3%
1 281283
28.4%
2 42299
 
4.3%
3 6813
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 660790
66.7%
Other Punctuation 330395
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 330395
50.0%
1 281283
42.6%
2 42299
 
6.4%
3 6813
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 330395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 991185
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 330395
33.3%
0 330395
33.3%
1 281283
28.4%
2 42299
 
4.3%
3 6813
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 991185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 330395
33.3%
0 330395
33.3%
1 281283
28.4%
2 42299
 
4.3%
3 6813
 
0.7%

EFAMTYPE
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2662639
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:21.289540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q311
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.4088466
Coefficient of variation (CV)0.86316931
Kurtosis-0.43990191
Mean6.2662639
Median Absolute Deviation (MAD)3
Skewness0.91044806
Sum2773298
Variance29.255622
MonotonicityNot monotonic
2024-05-17T15:21:21.384325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 82875
18.7%
3 82056
18.5%
11 62179
14.0%
2 56312
12.7%
18 36867
8.3%
4 22292
 
5.0%
5 21634
 
4.9%
6 17978
 
4.1%
8 16930
 
3.8%
14 13747
 
3.1%
Other values (8) 29706
 
6.7%
ValueCountFrequency (%)
1 82875
18.7%
2 56312
12.7%
3 82056
18.5%
4 22292
 
5.0%
5 21634
 
4.9%
6 17978
 
4.1%
7 4585
 
1.0%
8 16930
 
3.8%
9 6473
 
1.5%
10 3019
 
0.7%
ValueCountFrequency (%)
18 36867
8.3%
17 1593
 
0.4%
16 4078
 
0.9%
15 5728
 
1.3%
14 13747
 
3.1%
13 1401
 
0.3%
12 2829
 
0.6%
11 62179
14.0%
10 3019
 
0.7%
9 6473
 
1.5%

AGYOWNK
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing327294
Missing (%)74.0%
Memory size3.4 MiB
1.0
38621 
2.0
35128 
3.0
21506 
4.0
20027 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters345846
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 38621
 
8.7%
2.0 35128
 
7.9%
3.0 21506
 
4.9%
4.0 20027
 
4.5%
(Missing) 327294
74.0%

Length

2024-05-17T15:21:21.491530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:21:21.602553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 38621
33.5%
2.0 35128
30.5%
3.0 21506
18.7%
4.0 20027
17.4%

Most occurring characters

ValueCountFrequency (%)
. 115282
33.3%
0 115282
33.3%
1 38621
 
11.2%
2 35128
 
10.2%
3 21506
 
6.2%
4 20027
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 230564
66.7%
Other Punctuation 115282
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115282
50.0%
1 38621
 
16.8%
2 35128
 
15.2%
3 21506
 
9.3%
4 20027
 
8.7%
Other Punctuation
ValueCountFrequency (%)
. 115282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 345846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 115282
33.3%
0 115282
33.3%
1 38621
 
11.2%
2 35128
 
10.2%
3 21506
 
6.2%
4 20027
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 345846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115282
33.3%
0 115282
33.3%
1 38621
 
11.2%
2 35128
 
10.2%
3 21506
 
6.2%
4 20027
 
5.8%

FINALWT
Real number (ℝ)

Distinct2132
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.18954
Minimum1
Maximum3403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-17T15:21:21.715862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile61
Q1132
median212
Q3351
95-th percentile927
Maximum3403
Range3402
Interquartile range (IQR)219

Descriptive statistics

Standard deviation275.19033
Coefficient of variation (CV)0.91672193
Kurtosis6.5612545
Mean300.18954
Median Absolute Deviation (MAD)94
Skewness2.3161384
Sum1.3285668 × 108
Variance75729.719
MonotonicityNot monotonic
2024-05-17T15:21:21.841320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 1724
 
0.4%
131 1710
 
0.4%
132 1710
 
0.4%
142 1707
 
0.4%
136 1685
 
0.4%
139 1677
 
0.4%
138 1673
 
0.4%
134 1665
 
0.4%
141 1662
 
0.4%
135 1661
 
0.4%
Other values (2122) 425702
96.2%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 6
< 0.1%
5 4
 
< 0.1%
6 7
< 0.1%
7 14
< 0.1%
8 14
< 0.1%
9 13
< 0.1%
10 11
< 0.1%
ValueCountFrequency (%)
3403 1
< 0.1%
3202 1
< 0.1%
2795 1
< 0.1%
2737 1
< 0.1%
2736 1
< 0.1%
2735 1
< 0.1%
2684 1
< 0.1%
2683 1
< 0.1%
2667 1
< 0.1%
2666 1
< 0.1%

Interactions

2024-05-17T15:20:51.987274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:18:57.985836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:01.933025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.815956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:09.892491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.824053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.639324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.824739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:26.408273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:30.372508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:34.503066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:38.416748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:43.022890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:46.232826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.755375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.663581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.718952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:01.254569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.645102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:08.033796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.707448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:15.081118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:18.406045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:21.913111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:25.305092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.769871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.816575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:35.235861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:38.339466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:41.495505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.662550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:48.010727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:52.116307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:18:58.136927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:02.071091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.938152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:10.018566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.934724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.773753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.952686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:26.537609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:30.508137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:34.630391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:38.607296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:43.128598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:46.357160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.883983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.790388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.840805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:01.369611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.761185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:08.151122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.821863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:15.192100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:18.526168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:22.016410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:25.439988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.872400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.924702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:35.340205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:38.447669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-17T15:20:37.696711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:40.840477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.002729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:47.064659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:51.193117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:55.267209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:01.230807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.202577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:09.280542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.210035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.013392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.143003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:25.079616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:29.673918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:33.840512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:37.775494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:42.436565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:45.729181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.230725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.034125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.192672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:00.739685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.141513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:07.529528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.202551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:14.590749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:17.912814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:21.405857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:24.757316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.275534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.314069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:34.425937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:37.800034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:40.946650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.107596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:47.167476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:51.301147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:55.385607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:01.356950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.320267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:09.397906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.333291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.136898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.261262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:25.196776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:29.805849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:33.963575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:37.882327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:42.547082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:45.815247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.314823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.117428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.277304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:00.823119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.224203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:07.610741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.286463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:14.673363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:17.993798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:21.487267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:24.864481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.356687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.421476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:34.536544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:37.906264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:41.054815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.222216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:47.276231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:51.423846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:55.488130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:01.468755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.423866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:09.501984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.437595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.255474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.369814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:25.301505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:29.928080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:34.076660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:37.982396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:42.649182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:45.912843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.396247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.236990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.359799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:00.904528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.305827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:07.695119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.367458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:14.753742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:18.077134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:21.568240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:24.968334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.438967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.522251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:34.642136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:38.008708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:41.159629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.322756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:47.654436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:51.540165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:55.630098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:01.612427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.551139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:09.632223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.567617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.389275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.526757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:26.117588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:30.082208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:34.216776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:38.109873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:42.784453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:45.997266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.496202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.364694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.462276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:01.006943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.399373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:07.790773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.463233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:14.846974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:18.161152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:21.669716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:25.075041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.539545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.604690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:34.726378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:38.114547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:41.268943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.440834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:47.761898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:51.687516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:55.764783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:01.778438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:05.683676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:09.757425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:13.702784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:17.505170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:21.677904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:26.268525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:30.232027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:34.360216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:38.258891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:42.909021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:46.105571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:49.624696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:53.511407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:19:57.588597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:01.131374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:04.520948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:07.912187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:11.583203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:14.969177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:18.273530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:21.795152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:25.188692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:28.659379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:31.714234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:35.128055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:38.222783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:41.381255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:44.559362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:47.870695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:20:51.834051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-17T15:21:22.040255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AGE_12AGE_6AGYOWNKAHRSMAINATOTHRSAVAILABLCMACOWMAINDURJLESSDURUNEMPEDUCEFAMTYPEESTSIZEEVERWORKFINALWTFIRMSIZEFLOWUNEMFTPTLASTFTPTMAINHRLYEARNHRSAWAYIMMIGLFSSTATMARSTATMJHNAICS_21NOC_10NOC_43PAIDOTPAYAWAYPERMTEMPPREVTENPRIORACTPROVREC_NUMSCHOOLNSEXSURVMNTHTENURETLOLOOKUHRSMAINUNEMFTPTUNIONUNPAIDOTUTOTHRSWHYLEFTNWHYLEFTOWHYPTWKSAWAYXTRAHRSYABSENTYAWAYYNOLOOK
AGE_121.0000.9580.5030.0630.0580.285-0.0500.0580.6630.138-0.0030.0400.0950.458-0.1150.067-0.1590.3190.3930.2360.0110.1730.361-0.4190.031-0.055-0.105-0.1140.0040.2460.1350.5770.355-0.0390.0010.4870.0350.0000.5240.1230.0800.2560.1030.0740.0730.1590.3950.2290.0410.0560.2660.090-0.280
AGE_60.9581.0000.0340.3950.3940.1990.010-0.0860.1440.1440.678-0.1860.1330.3480.1090.058-0.1380.3500.5450.5510.0800.0580.255-0.4580.047-0.081-0.210-0.2560.1070.2940.1380.2650.3190.004-0.0000.4550.0170.0000.3120.1970.4870.3950.1340.1340.4840.062-0.0220.1310.3280.1650.2160.098-0.240
AGYOWNK0.5030.0341.0000.0630.0650.0440.048-0.0020.1370.010-0.0650.2760.0120.0820.0550.011-0.0940.0340.0130.004-0.0470.1300.0710.0010.0170.005-0.026-0.021-0.0290.2010.0230.2340.065-0.008-0.0020.0540.0110.0000.2730.017-0.0010.0330.0190.0370.0030.1090.2380.193-0.2850.0090.3410.086-0.121
AHRSMAIN0.0630.3950.0631.0000.9690.0000.0060.032NaNNaN0.076-0.0440.0770.0000.0030.064NaN0.0000.7200.259-0.3160.0440.785-0.1070.079-0.175-0.015-0.0350.3091.0000.147NaN0.0000.023-0.0010.2930.2360.0180.0960.0000.6800.0000.0930.2750.646NaNNaN0.072NaN0.4321.0000.074NaN
ATOTHRS0.0580.3940.0650.9691.0000.0000.0060.031NaNNaN0.083-0.0450.0750.0000.0020.062NaN0.0000.6990.245-0.3060.0390.795-0.1040.208-0.161-0.017-0.0360.2991.0000.145NaN0.0000.026-0.0000.2940.2280.0180.0870.0000.6520.0000.0900.2670.686NaNNaN0.080NaN0.4191.0000.071NaN
AVAILABL0.2850.1990.0440.0000.0001.000-0.0290.094-0.049NaN0.0570.0610.0000.059-0.0300.000NaN0.1210.000NaNNaN0.0370.765-0.1800.000-0.1010.0520.041NaN0.0000.0000.1471.000-0.023-0.0050.4520.0530.129NaN1.000NaN1.0000.000NaNNaN0.2070.147NaNNaNNaN0.0000.000NaN
CMA-0.0500.0100.0480.0060.006-0.0291.0000.051-0.0240.0210.1120.0280.0690.0870.4860.0350.0610.0780.0210.068-0.0200.2520.0260.0030.0260.031-0.093-0.098-0.0470.0580.021-0.0340.0620.3240.0010.0360.0000.002-0.0140.065-0.0100.0540.0800.025-0.010-0.027-0.0760.008-0.007-0.0160.0590.0730.011
COWMAIN0.058-0.086-0.0020.0320.0310.0940.0511.000-0.0000.007-0.1560.0060.3261.0000.0540.354-0.0200.0960.159-0.271-0.0600.0770.083-0.0210.041-0.3320.1090.105-0.0190.2180.1230.0160.0550.065-0.0030.0860.1970.000-0.0240.0370.1060.0750.595-0.1120.101-0.085-0.1450.083-0.067-0.0920.2270.104-0.006
DURJLESS0.6630.1440.137NaNNaN-0.049-0.024-0.0001.0000.484-0.072-0.0060.0000.843-0.0370.0000.4931.0000.000NaNNaN0.1130.432-0.2170.0000.072-0.066-0.059NaN0.0000.000-0.0490.200-0.011-0.0000.2590.1220.015NaN1.000NaN0.1160.000NaNNaN-0.235-0.068NaNNaNNaN0.0000.000-0.283
DURUNEMP0.1380.1440.010NaNNaNNaN0.0210.0070.4841.0000.0760.0390.0000.316-0.0080.000-0.0230.1390.000NaNNaN0.0391.000-0.0710.000-0.028-0.033-0.035NaN0.0000.0000.0550.089-0.003-0.0060.0730.0100.037NaN0.059NaN0.1160.000NaNNaN0.0460.065NaNNaNNaN0.0000.000NaN
EDUC-0.0030.678-0.0650.0760.0830.0570.112-0.156-0.0720.0761.000-0.1030.1220.2310.0680.101-0.0190.2050.2180.4320.0220.1300.173-0.2150.0500.161-0.343-0.376-0.0350.1830.0880.1890.2030.0180.0000.3260.0560.0000.0480.0000.0340.1980.1010.1920.0460.0210.0520.0690.0440.1120.1390.075-0.141
EFAMTYPE0.040-0.1860.276-0.044-0.0450.0610.0280.006-0.0060.039-0.1031.0000.0310.3340.0050.0270.0110.2570.093-0.089-0.0110.1140.315-0.1330.022-0.0010.0560.064-0.0260.0760.0400.0690.2790.007-0.0010.1390.0650.003-0.0380.000-0.0520.2070.040-0.031-0.0530.0300.045-0.0450.003-0.0410.1970.0660.021
ESTSIZE0.0950.1330.0120.0770.0750.0000.0690.3260.0000.0000.1220.0311.0000.0000.0420.509NaN0.0000.1560.3160.0290.0460.042-0.0820.0270.027-0.119-0.1280.0630.1970.047NaN0.000-0.0340.0010.0750.0320.0030.1600.0000.0760.0000.2150.0740.069NaNNaN-0.0240.0570.1010.0460.073NaN
EVERWORK0.4580.3480.0820.0000.0000.0590.0871.0000.8430.3160.2310.3340.0001.0000.0300.0000.4021.0000.000NaNNaN0.1410.4070.0450.000NaNNaNNaNNaN0.0000.000NaN0.3800.0450.0000.3190.1030.007NaN1.000NaN0.2170.000NaNNaNNaNNaNNaNNaNNaN0.0000.000-0.091
FINALWT-0.1150.1090.0550.0030.002-0.0300.4860.054-0.037-0.0080.0680.0050.0420.0301.0000.0070.0530.0190.0130.037-0.0220.0730.0330.0520.001-0.009-0.029-0.032-0.0240.0240.010-0.0610.0210.2800.0010.0200.0160.011-0.0790.084-0.0120.0180.0390.012-0.014-0.039-0.101-0.021-0.042-0.0080.0240.0080.004
FIRMSIZE0.0670.0580.0110.0640.0620.0000.0350.3540.0000.0000.1010.0270.5090.0000.0071.000NaN0.0000.1130.2390.0310.0220.043-0.0580.0220.062-0.104-0.1120.0530.2030.044NaN0.000-0.0220.0000.0310.0400.0020.1570.0000.0050.0000.2400.0930.002NaNNaN-0.0200.0550.1060.0510.079NaN
FLOWUNEM-0.159-0.138-0.094NaNNaNNaN0.061-0.0200.493-0.023-0.0190.011NaN0.4020.053NaN1.0000.2860.000NaNNaN0.1091.0000.0850.0000.162-0.100-0.078NaN0.0000.000-0.1500.5940.0870.0030.3120.1840.068NaN1.000NaN0.7610.000NaNNaN-0.486-0.394NaNNaNNaN0.0000.000NaN
FTPTLAST0.3190.3500.0340.0000.0000.1210.0780.0961.0000.1390.2050.2570.0001.0000.0190.0000.2861.0000.000NaNNaN0.0000.1830.1840.0000.222-0.065-0.033NaN0.0000.000-0.2050.3880.081-0.0090.3360.1460.015NaN0.025NaN0.4690.000NaNNaN-0.122-0.139NaNNaNNaN0.0000.000-0.008
FTPTMAIN0.3930.5450.0130.7200.6990.0000.0210.1590.0000.0000.2180.0930.1560.0000.0130.1130.0000.0001.000-0.366-0.0660.0370.0320.1500.0760.1120.0840.109-0.0940.2370.307NaN0.0000.017-0.0020.4940.1390.000-0.1640.000-0.6990.0000.090-0.092-0.668NaNNaNNaN-0.121-0.1370.2210.148NaN
HRLYEARN0.2360.5510.0040.2590.245NaN0.068-0.271NaNNaN0.432-0.0890.316NaN0.0370.239NaNNaN-0.3661.0000.0350.0560.019-0.2810.037-0.010-0.338-0.3830.0620.3130.091NaN0.0000.063-0.0010.1930.1270.0000.3450.0000.2580.0000.1610.2410.241NaNNaN0.035-0.0210.2210.1110.106NaN
HRSAWAY0.0110.080-0.047-0.316-0.306NaN-0.020-0.060NaNNaN0.022-0.0110.029NaN-0.0220.031NaNNaN-0.0660.0351.0000.0071.000-0.0200.0310.023-0.011-0.012-0.0110.0000.009NaN0.0000.014-0.0010.0240.0200.0090.0370.0000.0200.0000.0330.0000.024NaNNaN-0.030NaN-0.0110.0000.091NaN
IMMIG0.1730.0580.1300.0440.0390.0370.2520.0770.1130.0390.1300.1140.0460.1410.0730.0220.1090.0000.0370.0560.0071.0000.0620.1400.012-0.0020.0260.0260.0190.0520.032-0.0110.099-0.1590.0000.0620.0130.0020.0610.096-0.0400.0370.0490.038-0.0450.0170.069-0.011-0.0060.0400.1020.0360.004
LFSSTAT0.3610.2550.0710.7850.7950.7650.0260.0830.4321.0000.1730.3150.0420.4070.0330.0431.0000.1830.0320.0191.0000.0621.0000.0140.032-0.0130.1090.119NaN1.0000.0120.0441.000-0.0300.0020.2350.0920.0140.0431.000-0.0311.0000.067NaN-0.038-0.342-0.006-0.013NaNNaN1.0001.000NaN
MARSTAT-0.419-0.4580.001-0.107-0.104-0.1800.003-0.021-0.217-0.071-0.215-0.133-0.0820.0450.052-0.0580.0850.1840.150-0.281-0.0200.1400.0141.0000.0190.0410.1420.157-0.0180.1130.077-0.4200.238-0.0380.0010.3210.1440.000-0.2700.070-0.1410.1240.069-0.073-0.135-0.092-0.244-0.088-0.076-0.0680.1290.0680.142
MJH0.0310.0470.0170.0790.2080.0000.0260.0410.0000.0000.0500.0220.0270.0000.0010.0220.0000.0000.0760.0370.0310.0120.0320.0191.0000.054-0.018-0.016-0.0150.0000.037NaN0.0000.0230.0000.0180.0350.000-0.0440.000-0.0730.0000.0090.0020.183NaNNaN0.035-0.046-0.0110.0370.019NaN
NAICS_21-0.055-0.0810.005-0.175-0.161-0.1010.031-0.3320.072-0.0280.161-0.0010.027NaN-0.0090.0620.1620.2220.112-0.0100.023-0.002-0.0130.0410.0541.000-0.256-0.250-0.0690.2050.122-0.0870.141-0.022-0.0010.2020.3830.000-0.0150.039-0.2670.1910.3250.042-0.244-0.102-0.057-0.0400.033-0.0260.1790.102-0.092
NOC_10-0.105-0.210-0.026-0.015-0.0170.052-0.0930.109-0.066-0.033-0.3430.056-0.119NaN-0.029-0.104-0.100-0.0650.084-0.338-0.0110.0260.1090.142-0.018-0.2561.0000.9900.1160.2200.140-0.0860.148-0.0090.0010.2050.4830.000-0.1120.1530.0470.1930.279-0.1750.0410.065-0.0070.006-0.027-0.0380.2010.1280.102
NOC_43-0.114-0.256-0.021-0.035-0.0360.041-0.0980.105-0.059-0.035-0.3760.064-0.128NaN-0.032-0.112-0.078-0.0330.109-0.383-0.0120.0260.1190.157-0.016-0.2500.9901.0000.1150.2120.128-0.1150.147-0.0090.0020.2050.4830.000-0.1250.0920.0260.1910.284-0.1920.0220.051-0.020-0.004-0.025-0.0510.1740.1270.110
PAIDOT0.0040.107-0.0290.3090.299NaN-0.047-0.019NaNNaN-0.035-0.0260.063NaN-0.0240.053NaNNaN-0.0940.062-0.0110.019NaN-0.018-0.015-0.0690.1160.1151.0000.0000.021NaN0.0000.025-0.0000.0310.0830.0050.0320.0000.1370.0000.053-0.0350.128NaNNaN-0.010NaN0.6960.0000.013NaN
PAYAWAY0.2460.2940.2011.0001.0000.0000.0580.2180.0000.0000.1830.0760.1970.0000.0240.2030.0000.0000.2370.3130.0000.0521.0000.1130.0000.2050.2200.2120.0001.0000.180NaN0.000-0.001-0.0080.2010.0510.067-0.2270.000-0.1630.0000.158NaN-0.160NaNNaN-0.0120.300NaN0.3710.000NaN
PERMTEMP0.1350.1380.0230.1470.1450.0000.0210.1230.0000.0000.0880.0400.0470.0000.0100.0440.0000.0000.3070.0910.0090.0320.0120.0770.0370.1220.1400.1280.0210.1801.000NaN0.000-0.036-0.0020.1920.0510.008-0.2420.000-0.2320.0000.044-0.034-0.218NaNNaN0.009-0.095-0.0470.1010.082NaN
PREVTEN0.5770.2650.234NaNNaN0.147-0.0340.016-0.0490.0550.1890.069NaNNaN-0.061NaN-0.150-0.205NaNNaNNaN-0.0110.044-0.420NaN-0.087-0.086-0.115NaNNaNNaN1.0000.126-0.0160.0000.2600.0220.004NaN0.134NaN0.0840.000NaNNaN0.0290.302NaNNaNNaN0.0000.000-0.030
PRIORACT0.3550.3190.0650.0000.0001.0000.0620.0550.2000.0890.2030.2790.0000.3800.0210.0000.5940.3880.0000.0000.0000.0991.0000.2380.0000.1410.1480.1470.0000.0000.0000.1261.0000.0720.0050.4700.1910.028NaN0.000NaN0.4720.000NaNNaN-0.155-0.136NaNNaNNaN0.0000.000NaN
PROV-0.0390.004-0.0080.0230.026-0.0230.3240.065-0.011-0.0030.0180.007-0.0340.0450.280-0.0220.0870.0810.0170.0630.014-0.159-0.030-0.0380.023-0.022-0.009-0.0090.025-0.001-0.036-0.0160.0721.0000.0010.0260.0040.003-0.0130.0950.0410.0630.0800.0150.045-0.049-0.060-0.006-0.0180.0290.0890.084-0.056
REC_NUM0.001-0.000-0.002-0.001-0.000-0.0050.001-0.003-0.000-0.0060.000-0.0010.0010.0000.0010.0000.003-0.009-0.002-0.001-0.0010.0000.0020.0010.000-0.0010.0010.002-0.000-0.008-0.0020.0000.0050.0011.0000.0040.0020.018-0.0010.000-0.0010.0100.0050.005-0.0000.001-0.000-0.005-0.0020.0040.0070.007-0.015
SCHOOLN0.4870.4550.0540.2930.2940.4520.0360.0860.2590.0730.3260.1390.0750.3190.0200.0310.3120.3360.4940.1930.0240.0620.2350.3210.0180.2020.2050.2050.0310.2010.1920.2600.4700.0260.0041.0000.0340.004-0.2490.000-0.3450.4430.069-0.059-0.338-0.258-0.219-0.135-0.115-0.0900.2040.1030.356
SEX0.0350.0170.0110.2360.2280.0530.0000.1970.1220.0100.0560.0650.0320.1030.0160.0400.1840.1460.1390.1270.0200.0130.0920.1440.0350.3830.4830.4830.0830.0510.0510.0220.1910.0040.0020.0341.0000.000-0.0160.000-0.2710.1710.0660.031-0.256-0.123-0.064-0.0850.140-0.0490.2980.074-0.033
SURVMNTH0.0000.0000.0000.0180.0180.1290.0020.0000.0150.0370.0000.0030.0030.0070.0110.0020.0680.0150.0000.0000.0090.0020.0140.0000.0000.0000.0000.0000.0050.0670.0080.0040.0280.0030.0180.0040.0001.000-0.0050.0000.0060.0710.0000.0010.007-0.011-0.0030.009-0.0370.0020.0670.090-0.003
TENURE0.5240.3120.2730.0960.087NaN-0.014-0.024NaNNaN0.048-0.0380.160NaN-0.0790.157NaNNaN-0.1640.3450.0370.0610.043-0.270-0.044-0.015-0.112-0.1250.032-0.227-0.242NaNNaN-0.013-0.001-0.249-0.016-0.0051.0000.0000.1300.0000.1440.1080.116NaNNaN0.0850.0990.1010.1520.077NaN
TLOLOOK0.1230.1970.0170.0000.0001.0000.0650.0371.0000.0590.0000.0000.0001.0000.0840.0001.0000.0250.0000.0000.0000.0961.0000.0700.0000.0390.1530.0920.0000.0000.0000.1340.0000.0950.0000.0000.0000.0000.0001.000NaN0.0250.000NaNNaNNaNNaNNaNNaNNaN0.0000.000NaN
UHRSMAIN0.0800.487-0.0010.6800.652NaN-0.0100.106NaNNaN0.034-0.0520.076NaN-0.0120.005NaNNaN-0.6990.2580.020-0.040-0.031-0.141-0.073-0.2670.0470.0260.137-0.163-0.232NaNNaN0.041-0.001-0.345-0.2710.0060.130NaN1.0000.0000.1380.0430.946NaNNaN0.0870.0630.1370.2090.095NaN
UNEMFTPT0.2560.3950.0330.0000.0001.0000.0540.0750.1160.1160.1980.2070.0000.2170.0180.0000.7610.4690.0000.0000.0000.0371.0000.1240.0000.1910.1930.1910.0000.0000.0000.0840.4720.0630.0100.4430.1710.0710.0000.0250.0001.0000.000NaNNaN-0.186-0.071NaNNaNNaN0.0000.000NaN
UNION0.1030.1340.0190.0930.0900.0000.0800.5950.0000.0000.1010.0400.2150.0000.0390.2400.0000.0000.0900.1610.0330.0490.0670.0690.0090.3250.2790.2840.0530.1580.0440.0000.0000.0800.0050.0690.0660.0000.1440.0000.1380.0001.000-0.0200.030NaNNaN0.018-0.086-0.0970.0620.058NaN
UNPAIDOT0.0740.1340.0370.2750.267NaN0.025-0.112NaNNaN0.192-0.0310.074NaN0.0120.093NaNNaN-0.0920.2410.0000.038NaN-0.0730.0020.042-0.175-0.192-0.035NaN-0.034NaNNaN0.0150.005-0.0590.0310.0010.108NaN0.043NaN-0.0201.0000.042NaNNaN-0.026NaN0.6730.0000.009NaN
UTOTHRS0.0730.4840.0030.6460.686NaN-0.0100.101NaNNaN0.046-0.0530.069NaN-0.0140.002NaNNaN-0.6680.2410.024-0.045-0.038-0.1350.183-0.2440.0410.0220.128-0.160-0.218NaNNaN0.045-0.000-0.338-0.2560.0070.116NaN0.946NaN0.0300.0421.000NaNNaN0.0940.0540.1280.2060.091NaN
WHYLEFTN0.1590.0620.109NaNNaN0.207-0.027-0.085-0.2350.0460.0210.030NaNNaN-0.039NaN-0.486-0.122NaNNaNNaN0.017-0.342-0.092NaN-0.1020.0650.051NaNNaNNaN0.029-0.155-0.0490.001-0.258-0.123-0.011NaNNaNNaN-0.186NaNNaNNaN1.0000.639NaNNaNNaN0.0000.0000.134
WHYLEFTO0.395-0.0220.238NaNNaN0.147-0.076-0.145-0.0680.0650.0520.045NaNNaN-0.101NaN-0.394-0.139NaNNaNNaN0.069-0.006-0.244NaN-0.057-0.007-0.020NaNNaNNaN0.302-0.136-0.060-0.000-0.219-0.064-0.003NaNNaNNaN-0.071NaNNaNNaN0.6391.000NaNNaNNaN0.0000.0000.190
WHYPT0.2290.1310.1930.0720.080NaN0.0080.083NaNNaN0.069-0.045-0.024NaN-0.021-0.020NaNNaNNaN0.035-0.030-0.011-0.013-0.0880.035-0.0400.006-0.004-0.010-0.0120.009NaNNaN-0.006-0.005-0.135-0.0850.0090.085NaN0.087NaN0.018-0.0260.094NaNNaN1.0000.002-0.0230.2900.183NaN
WKSAWAY0.0410.328-0.285NaNNaNNaN-0.007-0.067NaNNaN0.0440.0030.057NaN-0.0420.055NaNNaN-0.121-0.021NaN-0.006NaN-0.076-0.0460.033-0.027-0.025NaN0.300-0.095NaNNaN-0.018-0.002-0.1150.140-0.0370.099NaN0.063NaN-0.086NaN0.054NaNNaN0.0021.000NaN0.3050.000NaN
XTRAHRS0.0560.1650.0090.4320.419NaN-0.016-0.092NaNNaN0.112-0.0410.101NaN-0.0080.106NaNNaN-0.1370.221-0.0110.040NaN-0.068-0.011-0.026-0.038-0.0510.696NaN-0.047NaNNaN0.0290.004-0.090-0.0490.0020.101NaN0.137NaN-0.0970.6730.128NaNNaN-0.023NaN1.0000.0000.016NaN
YABSENT0.2660.2160.3411.0001.0000.0000.0590.2270.0000.0000.1390.1970.0460.0000.0240.0510.0000.0000.2210.1110.0000.1021.0000.1290.0370.1790.2010.1740.0000.3710.1010.0000.0000.0890.0070.2040.2980.0670.1520.0000.2090.0000.0620.0000.2060.0000.0000.2900.3050.0001.0000.000NaN
YAWAY0.0900.0980.0860.0740.0710.0000.0730.1040.0000.0000.0750.0660.0730.0000.0080.0790.0000.0000.1480.1060.0910.0361.0000.0680.0190.1020.1280.1270.0130.0000.0820.0000.0000.0840.0070.1030.0740.0900.0770.0000.0950.0000.0580.0090.0910.0000.0000.1830.0000.0160.0001.000NaN
YNOLOOK-0.280-0.240-0.121NaNNaNNaN0.011-0.006-0.283NaN-0.1410.021NaN-0.0910.004NaNNaN-0.008NaNNaNNaN0.004NaN0.142NaN-0.0920.1020.110NaNNaNNaN-0.030NaN-0.056-0.0150.356-0.033-0.003NaNNaNNaNNaNNaNNaNNaN0.1340.190NaNNaNNaNNaNNaN1.000

Missing values

2024-05-17T15:20:56.121058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-17T15:20:58.352215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-17T15:21:07.099092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

REC_NUMSURVYEARSURVMNTHLFSSTATPROVCMAAGE_12AGE_6SEXMARSTATEDUCMJHEVERWORKFTPTLASTCOWMAINIMMIGNAICS_21NOC_10NOC_43YABSENTWKSAWAYPAYAWAYUHRSMAINAHRSMAINFTPTMAINUTOTHRSATOTHRSHRSAWAYYAWAYPAIDOTUNPAIDOTXTRAHRSWHYPTTENUREPREVTENHRLYEARNUNIONPERMTEMPESTSIZEFIRMSIZEDURUNEMPFLOWUNEMUNEMFTPTWHYLEFTOWHYLEFTNDURJLESSAVAILABLLKPUBAGLKEMPLOYLKRELSLKATADSLKANSADSLKOTHERNPRIORACTYNOLOOKTLOLOOKSCHOOLNEFAMTYPEAGYOWNKFINALWT
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